The potential for in-patient mortality reductions to drive cost savings through decreases in hospital length of stay and intensive care unit utilization: a propensity matched cohort analysis

The potential for in-patient mortality reductions to drive cost savings through decreases in hospital length of stay and intensive care unit utilization: a propensity matched cohort analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The potential for in-patient mortality reductions to drive cost savings through decreases in hospital length of stay and intensive care unit utilization: a propensity matched cohort analysis Joseph Beals IV, Samantha McInnis, Kathy Belk This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3934554/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Background The economics of healthcare increasingly compels hospitals to accompany quality initiatives with a financial business case. Quality programs are frequently implemented with the aim of identifying clinical deterioration and reducing avoidable mortality. However, low rates of inpatient mortality with a diverse etiology make quantifying cost savings from avoidable mortality reduction challenging. To establish a relationship between mortality and length of stay, a commonly accepted indicator of hospital cost, we evaluated total hospital and intensive care unit length of stay for inpatients who expired compared to similar patients who did not expire during their hospital stay. Methods We conducted a retrospective propensity-matched cohort analysis of hospital inpatients who did and did not expire using de-identified data from 24 hospitals representing six health systems across the United States. In addition to demographic measures, the Rothman Index patient condition score was used to ensure cohorts were matched in physiologic acuity. Multivariable regression models were used to estimate the impact of mortality on the primary outcomes of total and ICU length of stay. Results Matched cohorts of patients who did and did not expire each comprised 6,129 patients. Patients who expired had longer mean overall hospital length of stay (LOS) compared to those who did not (13.3 vs 8.3 days, p < 0.0001) as well as longer mean ICU LOS (7.2 vs 5.7 days, p < 0.0265). Multivariable general linear models identified a 51% increase in predicted LOS and 33% increase in ICU LOS for patients who expired in the hospital. Conclusion As length of stay measures are routinely quantified in financial terms by hospitals, this work suggests it may be possible to translate mortality reductions into length of stay reductions as an inferential step in deriving a financial return on investment for mortality-focused quality of care initiatives. Mortality Length of Stay Return on Investment Quality Improvement Early Warning Score System Figures Figure 1 Introduction Hospital quality initiatives often include a focus on avoidable mortality, reflecting measures that the Centers for Medicare and Medicaid Services, the Joint Commission, and the Agency for Healthcare Research and Quality have emphasized in quality-of-care assessments. 1 One area of focus to reduce avoidable mortality is timely identification of, and response to, clinical deterioration. 2 , 3 In support of these efforts, hospitals are increasingly investing in the implementation of early warning score (EWS) systems, which commonly report performance for mortality risk prediction 4 – 6 and which have been successfully incorporated into clinical processes to reduce mortality, 7 – 11 although consistent benefit from EWS deployments remains unclear. 12 However, operationalization of EWS technologies and associated care delivery processes entails significant expense; these include information technology and software technology costs, technical and informatics resources to set-up and maintain EWS systems, clinical care redesign efforts, end-user training and support, and the cost of time taken by end-users to review and interpret the EWS output. 13 Such investments face scrutiny given that widespread challenges in hospital financial performance 14 have increased pressure to demonstrate that hospital quality programs will yield a positive financial return on investment (ROI), despite this often being a difficult measure to quantify. 15 , 16 Directly calculating the ROI attributable to mortality reduction initiatives ideally involves a comparison of costs between patients who expire but could have been intervened on (“potentially avoidable mortalities”, sometimes termed failure to rescue 17 ) and a similar group who would have likely died without the interventions prompted by the initiative (“successfully avoided mortalities”). However, the relatively low rate of avoidable mortality 18 and the wide variation in diagnoses, medication and treatment plans, as well as non-clinical factors (e.g., patient-dictated goals of care and the influences of administrative and insurance status considerations) confound basic analyses seeking to generalize the costs of care associated with patient mortality. In contrast, overall patient length of stay (LOS) and intensive care unit (ICU) length of stay are both closely connected to costs of care and management of LOS is strongly associated with management of costs. 19 Varying methodologies are used to calculate the unit cost per day of hospitalization or unit cost per ICU day but these are nevertheless commonly reported Figs. 2 0–22 and numerous studies have shown that the primary cost driver in the ICU is LOS. 23 Therefore, to establish an approach for quantifying the ROI associated with inpatient mortality reduction we sought to elucidate the relationship between mortality and both overall as well as ICU LOS to understand if changes in mortality could be extrapolated to changes in LOS as a basis for estimating financial impact. Methods We conducted a retrospective propensity-matched cohort analysis in accordance with STROBE guidelines 24 using de-identified data for inpatients discharged between January 1, 2022 and October 31, 2023 from 24 hospitals across six health systems. Hospitals ranged from a 24-bed critical access hospital to a 1,500-bed academic medical center and represented six states from the west coast, mid-west, southwest, southeast, and northeast United States. Inclusion criteria were patients 18 years of age or older admitted to either a routine or intermediate care unit. Patients directly admitted to an ICU were excluded to focus on patients with an opportunity to deteriorate or who were more likely to constitute avoidable mortalities – the primary target of general EWS systems. 5 Patients discharged to hospice and patients without a Rothman Index (RI) score (Spacelabs Healthcare, Snoqualmie, WA) during their stay were also excluded from the analysis. Administrative discharge codes were used to identify inpatient mortality and discharge to hospice. Outcomes of interest were overall inpatient LOS and ICU LOS, both calculated using Admission, Discharge and Transfer system data from health system electronic medical record systems. Propensity matching was used to control for differences across cohorts. 25 – 27 Matching covariates included patient age and sex as well as the first RI score during the visit. The RI, a widely validated, commercially available machine learning-based score of patient condition was used to control for differences in physiologic acuity. 28 Previous studies have shown the RI to be well-calibrated across the spectrum of patient acuity and effective at stratifying patient risk. 29 , 30 Logistic regression techniques were used to identify the cumulative probability of mortality using the matching covariates. Cases of patients who expired were then matched to controls based on these probabilities using a 1:1 Greedy matching algorithm. This algorithm attempts to match cases with the highest precision match first and continues to perform matches until no additional matches are found thereby minimizing the number of incomplete and inexact matches. 31 Matching processes were conducted separately for all six health systems and the post-match data was combined across all facilities for analysis. Baseline demographics including patient characteristics (e.g., sex, age, race, ethnicity), visit characteristics (e.g., admit type, discharge status) and clinical features representing clinical status (e.g., first RI score, surgery during stay) were reported and compared across cohorts before and after the matching process. Counts and percentages were used to report and compare categorical outcomes across cohorts while mean, median, and standard deviation were used to compare continuous variables such as overall and ICU specific LOS. Chi-square tests were used to analyze differences between cohorts for categorical variables with Fisher’s exact test used for comparisons with small sample sizes. For continuous variables, ANOVA was used to analyze differences between cohorts with Mann-Whitney tests used for non-normal distributions. Multivariable regression models were used to estimate the impact of mortality on study outcomes. General linear regression models with negative binomial distributions were used to evaluate both overall LOS and ICU specific LOS. Model covariates included patient and visit characteristics and clinical features indicating severity of illness and physiological status. All statistical tests were conducted using SAS version 9.4 (SAS Institute, Cary, NC). Alpha was set at 0.05 for tests of significance. Results Our study population (Fig. 1 ) included 493,105 patients 18 years old and above. Of these, 12,165 were excluded as hospice discharges, a further 44,017 were excluded as direct ICU admits, and 54,441 were excluded due to not having RI scores. The final study population consisted of 382,482 adult inpatients with a mortality rate of 1.9% Within this population, patients who expired were older (74.2 vs 59.1 years old, p < 0.0001) and had lower RI scores upon admission to the hospital (x̄ = 46.9 vs x̄ = 75.0, p < 0.0001) and were less likely to have surgery during their stay (8.7% vs 13.4%, p < 0.0001). The study cohorts were stratified by propensity matching into two equal groups of 6,129 patients. Table 1 reports the patient demographic, visit, and clinical characteristics of the study population prior to and after the matching process. As shown in Table 2 , patients who expired had longer mean overall hospital LOS (13.3 vs 8.3 days, p < 0.0001) as well as longer median LOS (8.1 vs 5.1 days, p < 0.0001) compared to those who did not. ICU specific LOS was significantly longer when comparing mean ICU LOS across cohorts (7.2 vs 5.7 days, p < 0.0265), however this difference was not statistically significant when evaluating median LOS (3.6 vs 3.2 days, p = 0.6416). Table 1. Patient demographic data pre- and post- propensity match. PRE-MATCH POST-MATCH Not Expired Expired p Value Not Expired Expired p Value Number of Patients 380,405 7,176 6,129 6,129 Sex Female 57.8% 48.3% <0.0001 47.6% 48.7% 0.2473 Male 42.2% 51.7% 52.4% 51.3% Race American Indian 0.5% 0.4% <0.0001 0.4% 0.4% 0.0009 Asian 2.7% 3.2% 2.3% 3.1% Black or African American 14.7% 11.2% 12.4% 11.4% Hawaiian or Pacific Islander 0.4% 0.4% 0.3% 0.4% Multi-Racial 2.3% 1.6% 1.2% 1.7% Other 1.3% 1.7% 2.4% 2.2% Unknown/Missing 6.6% 4.5% 3.3% 4.1% White 71.5% 77.2% 77.8% 76.8% Ethnicity Hispanic or Latino 12.4% 7.4% <0.0001 6.8% 7.4% 0.0002 Non-Hispanic or Latino 84.8% 89.8% 91.4% 89.8% Unknown 2.8% 2.8% 1.8% 2.8% Age at Admission (Years) 18-29 10.1% 0.5% <0.0001 0.4% 0.5% 0.4825 30-39 13.0% 1.5% 1.6% 1.7% 40-49 9.2% 3.3% 3.0% 3.5% 50-59 13.1% 8.8% 8.7% 9.3% 60-69 18.1% 19.1% 19.9% 19.8% 70-79 19.1% 27.7% 28.7% 28.4% 80-89 13.2% 26.6% 26.9% 26.2% 90+ 4.1% 12.4% 10.9% 10.6% Mean 59.1 74.2 <0.0001 74.0 73.4 0.0275 Admit through ED No 53.3% 50.9% <0.0001 46.8% 51.2% <0.0001 Yes 46.7% 49.1% 53.2% 48.8% Admit Type Elective 22.3% 9.9% <0.0001 9.9% 9.2% <0.0001 Emergency 70.5% 85.7% 86.9% 86.3% Trauma 0.7% 0.6% 0.9% 0.6% Urgent 6.3% 3.7% 0.2% 0.1% Unknown/Missing 0.1% 0.6% 2.2% 3.7% Discharge Status Expired 0.0% 100.0% <0.0001 0.0% 100.0% <0.0001 Home 66.2% 0.0% 35.1% 0.0% Home Health 14.7% 0.0% 24.4% 0.0% Intermediate Care 0.3% 0.0% 0.5% 0.0% Other 2.2% 0.0% 1.9% 0.0% Rehab 2.0% 0.0% 3.5% 0.0% Skilled Nursing Facility 12.9% 0.0% 32.5% 0.0% Transfer to Another Facility 1.7% 0.0% 2.2% 0.0% Admit Rothman Index <20 0.4% 12.2% <0.0001 3.0% 3.3% 0.9774 20-29 1.0% 10.6% 8.6% 8.9% 30-39 2.4% 13.4% 14.4% 14.2% 40-49 4.8% 16.1% 17.9% 18.1% 50-59 8.6% 15.4% 17.9% 17.8% 60-69 14.6% 14.9% 17.8% 17.5% 70-79 21.2% 10.8% 12.7% 12.6% 80+ 47.1% 6.5% 7.7% 7.6% Mean 75.0 46.9 <0.0001 52.7 52.4 0.4509 Surgery During Stay No 86.6% 91.3% <0.0001 89.5% 90.5% 0.0863 Yes 13.4% 8.7% 10.5% 9.5% Admit Unit Type Intermediate Care 10.6% 24.4% <0.0001 17.7% 23.0% <0.0001 Routine Care 82.8% 71.2% 76.1% 72.8% Telemetry 1.2% 1.0% 0.9% 1.0% Other 5.4% 3.5% 5.3% 3.2% Table 2 Post-Match Univariate Analysis. Not Expired Expired p Value Overall Length of Stay (Days) Mean 8.5 13.3 < 0.0001 Std Dev 12.6 22.7 Median 5.8 8.1 < 0.0001 ICU Length of Stay (Days) Mean 5.7 7.2 0.0265 Std Dev 10.0 13.7 Median 3.2 3.6 0.6416 In multivariable analysis, after controlling for patient and visit characteristics, patients who expired were predicted to have a 51% longer overall hospital LOS (Incident Rate Ratio (IRR) = 1.505, 95% CI = 1.458–1.555, p < 0.0001). Other factors associated with longer total LOS included urgent (IRR = 1.380, 95% CI = 1.246–1.530, p < 0.0001) and emergency admissions (IRR = 1.152, 95% CI = 1.091–1.216, p < 0.0001) as well as surgery occurring during the hospital stay (IRR = 1.953, 95% CI = 1.852–2.059, p < 0.0001). Factors associated with shorter hospital LOS were female sex (IRR = 0.922 (0.893–0.951, p < 0.0001), higher RI scores which represent lower acuity (IRR = 0.997, 95% CI = 0.996–0.998, p < 0.0001) and higher ages (IRR = 0.989, 95% CI = 0.987–0.990, p < 0.0001). IRRs for all variables are shown in Table 3 . Table 3 Negative binomial regression model for hospital length of stay. Parameter Value Reference Value Estimate p Value IRR (95% CI) Intercept 2.995 < 0.0001 Mortality Yes No 0.409 < 0.0001 1.505 (1.458–1.555) Sex Female Male -0.081 < 0.0001 0.922 (0.893–0.951) Admit Rothman Index -0.003 < 0.0001 0.997 (0.996–0.998) Patient Age (Years) -0.012 < 0.0001 0.989 (0.987–0.990) Admission type Emergency Elective 0.141 < 0.0001 1.152 (1.091–1.216) Admission type Trauma Elective -0.060 0.533 0.942 (0.780–1.137) Admission type Unknown Elective 0.469 0.018 1.599 (1.082–2.362) Admission type Urgent Elective 0.322 < 0.0001 1.380 (1.246–1.530) Admit Unit Type Intermediate Care Routine 0.004 0.851 1.004 (0.965–1.044) Admit Unit Type Telemetry Routine -0.255 0.002 0.775 (0.658–0.911) Surgery During Stay Yes No 0.669 < 0.0001 1.953 (1.852–2.059) In-hospital mortality was associated with a 33% increase in ICU LOS (IRR = 1.334, 95% CI = 1.180–1.508, p < 0.0001). Surgery during stay (IRR = 1.508, 95% CI = 1.345–1.690, p < 0.0001) was also associated with a predicted increase in LOS. Emergency admissions (IRR = 0.801, 95% CI = 0.689–0.932, p = 0.004) were associated with shorter ICU LOS along with higher patient age (IRR = 0.987, 95% CI = 0.984–0.991, p < 0.0001). IRRs for all variables are shown in Table 4 . Table 4 Negative binomial regression model for ICU length of stay. Parameter Value Reference Value Estimate p Value IRR (95% CI) Intercept 2.736 < 0.0001 Mortality Yes No 0.288 < 0.0001 1.334 (1.180–1.508) Sex Female Male -0.075 0.069 0.928 (0.855–1.006) Admit Rothman Index 0.001 0.595 1.001 (0.998–1.003) Patient Age (Years) -0.013 < 0.0001 0.987 (0.984–0.991) Admission type Emergency Elective -0.222 0.004 0.801 (0.689–0.932) Admission type Trauma Elective -0.249 0.310 0.779 (0.482–1.261) Admission type Unknown Elective 0.395 0.476 1.484 (0.501–4.396) Admission type Urgent Elective -0.059 0.616 0.942 (0.748–1.188) Admit Unit Type Intermediate Care Routine 0.000 0.999 1.000 (0.908–1.101) Admit Unit Type Telemetry Routine -0.092 0.677 0.912 (0.592–1.406) Surgery During Stay Yes No 0.411 < 0.0001 1.508 (1.345–1.690) Discussion Recognizing a growing need to justify expenditures on quality improvement programs that have a focus on avoidable mortality, our study sought to understand the association of mortality with overall LOS and ICU LOS. Specifically, we evaluated patients admitted to lower level of care settings, a population targeted by many mortality reduction programs including those using EWS 5 . We found that patients who expired had significantly longer LOS, both overall and in the ICU. We also found that patients with urgent and emergency admissions, as well as surgical procedures, had longer LOS, and the latter two factors were also associated with longer ICU LOS. Complexity of correlations between mortality and LOS have been reported elsewhere, and causal relationships and interacting clinical considerations are not simple to delineate. 32 In light of the range of factors associated with LOS measures, a strength of our analysis is its patient-level nature 33 and use of the RI to ensure cohorts were of comparable acuity at the time of admission to account for the relation between baseline physiological acuity and mortality risk. 1 , 34 Individual hospitals may find that the magnitude of the LOS difference between expired and non-expired patients in their populations is different from what we report here, and indeed additional matching criteria may be considered for deriving comparison cohorts. However, the magnitude of the difference we have found in both overall and ICU LOS for patients with comparable physiologic acuity on admission as measured by the RI patient condition score strongly suggests that a non-negligible LOS difference exists. Hospital-based quality improvement initiatives face a growing need to demonstrate a robust business case justification and an argument for cost effectiveness. In particular, quality initiatives that require significant investment, such as operationalizing EWS for more timely detection and intervention on deteriorating patients, can have laudable quality goals yet struggle to translate even unambiguous success into a clear financial benefit. Hence, we posit that a rigorously established association between mortality and LOS may provide a means for organizations to extrapolate a reduction in both overall and ICU LOS from reductions in inpatient mortality. This would then allow readily available cost-per-day models to be brought to bear for the purpose of computing a financial ROI attributable to a reduction in avoidable mortality. Furthermore, we note that the ability to infer a reduction in LOS from a reduction in avoidable mortality circumvents two associated LOS measurement challenges. First, while the LOS savings from a program focused on reducing avoidable mortality may be substantial within the specific target population, the volume of this target population (deaths and avoided deaths) is small (typically a few percent) relative to the total acute-care population, making it quantitatively challenging to discern an LOS change due to mortality reduction initiatives within total population-level LOS statistics. Second, variations in population acuity over time (which may be as simple as a more severe flu season from one year to another), variations in populations served (e.g. shifts in regional acute-care capacity, staffing and specialty service capacity, inter-hospital transfer patterns, ambulance diversion rates, etc.) and the fact that multiple contemporaneous operational changes are frequently made which have the potential, if not explicit aim, of influencing LOS, make causal attribution of changes to population-level LOS an uncertain proposition. Identifying and isolating initiatives likely to bear on mortality rates is a more manageable task, while possible acuity changes in a focused population can be accounted for using risk adjustment or acuity matching methodologies. Our study has limitations. We lacked data on patient goals of care, comfort measure status, and advanced directives which, if included as covariates, could nuance the populations used in the matched cohorts. We did not include diagnosis in the analysis, and while the Rothman Index was built and validated as a diagnosis agnostic patient condition acuity score, we recognize that at a granular patient level the course of care, decisions related to treatment in the ICU, and overall LOS may have a dependence on diagnosis and comorbidities. Additionally, while we conducted the matching process separately for each health system, combining data in the final analysis may have obscured facility-level differences in LOS measures between cohorts. We anticipate future work will entail expanding the methodological approach presented in this work to include segmenting populations by clinical diagnosis and/or comorbidities. Extending our analysis to evaluate the costs of care (or cost-per-day) specific to the patient sub-populations in our matched cohort groups may also elucidate important differences between generalized cost-per-day figures and the costs pertaining to our analysis populations (i.e. average costs are likely to be different in selected sub-population as a function of variations in acuity, treatment plans, goals of care etc.). Conclusions Financial pressures increasingly require that quality initiatives demonstrate a positive ROI. Reducing avoidable mortality is a well-established quality of care goal, but one which requires an investment of resources that is not straightforward to justify on a purely financial basis. This work demonstrates a statistically rigorous approach to inferring LOS reductions from mortality reductions as a possible path to calculating a financial ROI for programs that are successful in their quest to improve avoidable mortality rates. Abbreviations EWS Early Warning Score LOS Length of Stay ICU Intensive Care Unit RI Rothman Index ROI Return on Investment Declarations Acknowledgments Not applicable. Authors’ contributions J.B. and K.B. conceived and designed the study; K.B. performed data extraction and preparation and K.B. and S.M. conducted statistical analyses; J.B., K.B. and S.M. interpreted the results and J.B. and K.B. drafted the manuscript. All authors approved the final manuscript. Funding Authors are employees of Spacelabs Healthcare. Neither Spacelabs nor Spacelabs affiliated authors received additional funding or compensation for this study. Data Availability Due to confidentiality and data use agreements, the datasets analyzed in the current study are not publicly available. Requests to access the datasets for bona fide research purposes should be directed to the corresponding author. Ethics approval and consent to participate Not applicable as this work entailed retrospective research using health information which was de-identified in accordance with 45 CFR 164.502(d) and 45 CFR 164.514(a)-(b). Consent for publication Not applicable. Competing interests The authors are employed by Spacelabs Healthcare, the commercial entity behind the Rothman Index patient condition score which was used in this analysis. References Truxillo TM, Schubert A, Guthrie R. Importance of Risk-Adjusted Mortality in Hospital Quality Rankings. In: Schubert A, Kemmerly SA, eds. Optimizing Widely Reported Hospital Quality and Safety Grades: An Ochsner Quality and Value Playbook . Springer International Publishing; 2022:209-215. doi:10.1007/978-3-031-04141-9_25 Padilla RM, Mayo AM. Clinical deterioration: A concept analysis. J Clin Nurs . 2018;27(7-8):1360-1368. doi:10.1111/jocn.14238 Churpek MM, Wendlandt B, Zadravecz FJ, Adhikari R, Winslow C, Edelson DP. 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Evaluation of hospital outcomes: the relation between length-of-stay, readmission, and mortality in a large international administrative database. BMC Health Serv Res . 2018;18(1):116. doi:10.1186/s12913-018-2916-1 Hofstede SN, van Bodegom-Vos L, Kringos DS, Steyerberg E, Marang-van de Mheen PJ. Mortality, readmission and length of stay have different relationships using hospital-level versus patient-level data: an example of the ecological fallacy affecting hospital performance indicators. BMJ Qual Saf . 2018;27(6):474-483. doi:10.1136/bmjqs-2017-006776 Girling AJ, Hofer TP, Wu J, et al. Case-mix adjusted hospital mortality is a poor proxy for preventable mortality: a modelling study. BMJ Qual Saf . 2012;21(12):1052-1056. doi:10.1136/bmjqs-2012-001202 Additional Declarations Competing interest reported. The authors are employed by Spacelabs Healthcare, the commercial entity behind the Rothman Index patient condition score which was used in this analysis. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 26 Feb, 2024 Reviewers agreed at journal 19 Feb, 2024 Reviewers invited by journal 18 Feb, 2024 Editor invited by journal 15 Feb, 2024 Submission checks completed at journal 15 Feb, 2024 Editor assigned by journal 15 Feb, 2024 First submitted to journal 06 Feb, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3934554","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":273068662,"identity":"160eb978-6184-40cc-a1f4-1901e4c44795","order_by":0,"name":"Joseph Beals IV","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8UlEQVRIiWNgGAWjYBACxgYwcYCBgZ2B8QGQLcPAwEasFmYGZgMGBgMeglqg+sBa2CSI0sLc3v7wceGOO3L8zezPKj62/eHhZ29LYPhRsQ23BT1njI1nnnlmLHGYx+zmzDYDHsmeYweAordxa5mRwybN23Y4seEwD9ttXqAWgxvpDcyMbfi0pD//DdRSP/8w+7NiIrUkmDEDtSQYHGYAMUBa0g7g1wL0i/TMtmeGGw/zGEvOOGcM8kvCQXx+MQSG2OfCtjvycsfbH374UCYnBwwxwwc/KvBoaQDFCDo4gFM9EMgzYNMyCkbBKBgFowAZAAAA6FfpknRdogAAAABJRU5ErkJggg==","orcid":"","institution":"Spacelabs Healthcare","correspondingAuthor":true,"prefix":"","firstName":"Joseph","middleName":"Beals","lastName":"IV","suffix":""},{"id":273068663,"identity":"a4076b0e-38d7-4b66-a097-e54d2bca409e","order_by":1,"name":"Samantha McInnis","email":"","orcid":"","institution":"Spacelabs Healthcare","correspondingAuthor":false,"prefix":"","firstName":"Samantha","middleName":"","lastName":"McInnis","suffix":""},{"id":273068664,"identity":"05fa62ae-5e98-4901-a70a-86db4e61488a","order_by":2,"name":"Kathy Belk","email":"","orcid":"","institution":"Spacelabs Healthcare","correspondingAuthor":false,"prefix":"","firstName":"Kathy","middleName":"","lastName":"Belk","suffix":""}],"badges":[],"createdAt":"2024-02-06 17:04:38","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3934554/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3934554/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":51238167,"identity":"6301ced4-9a23-4ce2-b1a3-126006e52c0c","added_by":"auto","created_at":"2024-02-16 16:48:19","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":73259,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStudy population attrition diagram.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-3934554/v1/ba44a9b9ca89bc4127fd11d6.png"},{"id":51238900,"identity":"06cbb1ea-97da-4aee-baa4-1689605c9b26","added_by":"auto","created_at":"2024-02-16 16:56:19","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":425797,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3934554/v1/49039f0e-b6dd-4ba5-b874-cd4bb64a486d.pdf"}],"financialInterests":"Competing interest reported. The authors are employed by Spacelabs Healthcare, the commercial entity behind the Rothman Index patient condition score which was used in this analysis.","formattedTitle":"The potential for in-patient mortality reductions to drive cost savings through decreases in hospital length of stay and intensive care unit utilization: a propensity matched cohort analysis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHospital quality initiatives often include a focus on avoidable mortality, reflecting measures that the Centers for Medicare and Medicaid Services, the Joint Commission, and the Agency for Healthcare Research and Quality have emphasized in quality-of-care assessments.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e One area of focus to reduce avoidable mortality is timely identification of, and response to, clinical deterioration.\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e In support of these efforts, hospitals are increasingly investing in the implementation of early warning score (EWS) systems, which commonly report performance for mortality risk prediction\u003csup\u003e\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e and which have been successfully incorporated into clinical processes to reduce mortality,\u003csup\u003e\u003cspan additionalcitationids=\"CR8 CR9 CR10\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e although consistent benefit from EWS deployments remains unclear.\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e However, operationalization of EWS technologies and associated care delivery processes entails significant expense; these include information technology and software technology costs, technical and informatics resources to set-up and maintain EWS systems, clinical care redesign efforts, end-user training and support, and the cost of time taken by end-users to review and interpret the EWS output.\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eSuch investments face scrutiny given that widespread challenges in hospital financial performance\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e have increased pressure to demonstrate that hospital quality programs will yield a positive financial return on investment (ROI), despite this often being a difficult measure to quantify.\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e Directly calculating the ROI attributable to mortality reduction initiatives ideally involves a comparison of costs between patients who expire but could have been intervened on (\u0026ldquo;potentially avoidable mortalities\u0026rdquo;, sometimes termed failure to rescue\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e) and a similar group who would have likely died without the interventions prompted by the initiative (\u0026ldquo;successfully avoided mortalities\u0026rdquo;). However, the relatively low rate of avoidable mortality\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e and the wide variation in diagnoses, medication and treatment plans, as well as non-clinical factors (e.g., patient-dictated goals of care and the influences of administrative and insurance status considerations) confound basic analyses seeking to generalize the costs of care associated with patient mortality.\u003c/p\u003e \u003cp\u003eIn contrast, overall patient length of stay (LOS) and intensive care unit (ICU) length of stay are both closely connected to costs of care and management of LOS is strongly associated with management of costs.\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e Varying methodologies are used to calculate the unit cost per day of hospitalization or unit cost per ICU day but these are nevertheless commonly reported Figs.\u0026nbsp;2\u003csup\u003e0\u0026ndash;22\u003c/sup\u003e and numerous studies have shown that the primary cost driver in the ICU is LOS.\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eTherefore, to establish an approach for quantifying the ROI associated with inpatient mortality reduction we sought to elucidate the relationship between mortality and both overall as well as ICU LOS to understand if changes in mortality could be extrapolated to changes in LOS as a basis for estimating financial impact.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eWe conducted a retrospective propensity-matched cohort analysis in accordance with STROBE guidelines\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e using de-identified data for inpatients discharged between January 1, 2022 and October 31, 2023 from 24 hospitals across six health systems. Hospitals ranged from a 24-bed critical access hospital to a 1,500-bed academic medical center and represented six states from the west coast, mid-west, southwest, southeast, and northeast United States. Inclusion criteria were patients 18 years of age or older admitted to either a routine or intermediate care unit. Patients directly admitted to an ICU were excluded to focus on patients with an opportunity to deteriorate or who were more likely to constitute avoidable mortalities \u0026ndash; the primary target of general EWS systems.\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e Patients discharged to hospice and patients without a Rothman Index (RI) score (Spacelabs Healthcare, Snoqualmie, WA) during their stay were also excluded from the analysis. Administrative discharge codes were used to identify inpatient mortality and discharge to hospice.\u003c/p\u003e \u003cp\u003eOutcomes of interest were overall inpatient LOS and ICU LOS, both calculated using Admission, Discharge and Transfer system data from health system electronic medical record systems.\u003c/p\u003e \u003cp\u003ePropensity matching was used to control for differences across cohorts.\u003csup\u003e\u003cspan additionalcitationids=\"CR26\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e Matching covariates included patient age and sex as well as the first RI score during the visit. The RI, a widely validated, commercially available machine learning-based score of patient condition was used to control for differences in physiologic acuity.\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e Previous studies have shown the RI to be well-calibrated across the spectrum of patient acuity and effective at stratifying patient risk.\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e,\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eLogistic regression techniques were used to identify the cumulative probability of mortality using the matching covariates. Cases of patients who expired were then matched to controls based on these probabilities using a 1:1 Greedy matching algorithm. This algorithm attempts to match cases with the highest precision match first and continues to perform matches until no additional matches are found thereby minimizing the number of incomplete and inexact matches.\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e Matching processes were conducted separately for all six health systems and the post-match data was combined across all facilities for analysis. Baseline demographics including patient characteristics (e.g., sex, age, race, ethnicity), visit characteristics (e.g., admit type, discharge status) and clinical features representing clinical status (e.g., first RI score, surgery during stay) were reported and compared across cohorts before and after the matching process.\u003c/p\u003e \u003cp\u003eCounts and percentages were used to report and compare categorical outcomes across cohorts while mean, median, and standard deviation were used to compare continuous variables such as overall and ICU specific LOS. Chi-square tests were used to analyze differences between cohorts for categorical variables with Fisher\u0026rsquo;s exact test used for comparisons with small sample sizes. For continuous variables, ANOVA was used to analyze differences between cohorts with Mann-Whitney tests used for non-normal distributions.\u003c/p\u003e \u003cp\u003eMultivariable regression models were used to estimate the impact of mortality on study outcomes. General linear regression models with negative binomial distributions were used to evaluate both overall LOS and ICU specific LOS. Model covariates included patient and visit characteristics and clinical features indicating severity of illness and physiological status.\u003c/p\u003e \u003cp\u003eAll statistical tests were conducted using SAS version 9.4 (SAS Institute, Cary, NC). Alpha was set at 0.05 for tests of significance.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eOur study population (Fig.\u0026nbsp;\u003cspan\u003e1\u003c/span\u003e) included 493,105 patients 18 years old and above. Of these, 12,165 were excluded as hospice discharges, a further 44,017 were excluded as direct ICU admits, and 54,441 were excluded due to not having RI scores. The final study population consisted of 382,482 adult inpatients with a mortality rate of 1.9%\u003c/p\u003e\n\u003cp\u003eWithin this population, patients who expired were older (74.2 vs 59.1 years old, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) and had lower RI scores upon admission to the hospital (x̄ = 46.9 vs x̄ = 75.0, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) and were less likely to have surgery during their stay (8.7% vs 13.4%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). The study cohorts were stratified by propensity matching into two equal groups of 6,129 patients. Table\u0026nbsp;\u003cspan\u003e1\u003c/span\u003e reports the patient demographic, visit, and clinical characteristics of the study population prior to and after the matching process.\u003c/p\u003e\n\u003cp\u003eAs shown in Table \u003cspan\u003e2\u003c/span\u003e, patients who expired had longer mean overall hospital LOS (13.3 vs 8.3 days, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) as well as longer median LOS (8.1 vs 5.1 days, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) compared to those who did not. ICU specific LOS was significantly longer when comparing mean ICU LOS across cohorts (7.2 vs 5.7 days, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0265), however this difference was not statistically significant when evaluating median LOS (3.6 vs 3.2 days, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.6416).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1.\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ePatient demographic data pre- and post- propensity match.\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"604\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.082644628099175%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.71900826446281%\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003ePRE-MATCH\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.19834710743802%\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003ePOST-MATCH\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.082644628099175%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.900826446280991%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNot Expired\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.40495867768595%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eExpired\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.413223140495868%\"\u003e\n \u003cp\u003e\u003cstrong\u003ep Value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.892561983471074%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eNot Expired\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.892561983471074%\"\u003e\n \u003cp\u003e\u003cstrong\u003eExpired\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.413223140495868%\"\u003e\n \u003cp\u003e\u003cstrong\u003ep Value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.966887417218544%\" valign=\"bottom\"\u003e\n \u003cp\u003eNumber of Patients\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.741721854304636%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e380,405\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.602649006622517%\" valign=\"bottom\"\u003e\n \u003cp\u003e7,176\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.245033112582782%\" colspan=\"2\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.099337748344372%\" valign=\"bottom\"\u003e\n \u003cp\u003e6,129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.913907284768213%\" valign=\"bottom\"\u003e\n \u003cp\u003e6,129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.43046357615894%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.082644628099175%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.71900826446281%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.586776859504132%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.223140495867769%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.082644628099173%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.892561983471074%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.413223140495868%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.082644628099175%\" valign=\"bottom\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.71900826446281%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e57.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.586776859504132%\" valign=\"bottom\"\u003e\n \u003cp\u003e48.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.223140495867769%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.082644628099173%\" valign=\"bottom\"\u003e\n \u003cp\u003e47.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.892561983471074%\" valign=\"bottom\"\u003e\n \u003cp\u003e48.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.413223140495868%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.2473\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.082644628099175%\" valign=\"bottom\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.71900826446281%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e42.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.586776859504132%\" valign=\"bottom\"\u003e\n \u003cp\u003e51.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.223140495867769%\" colspan=\"2\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.082644628099173%\" valign=\"bottom\"\u003e\n \u003cp\u003e52.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.892561983471074%\" valign=\"bottom\"\u003e\n \u003cp\u003e51.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.413223140495868%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.082644628099175%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eRace\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.71900826446281%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.586776859504132%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.223140495867769%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.082644628099173%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.892561983471074%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.413223140495868%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.082644628099175%\" valign=\"bottom\"\u003e\n \u003cp\u003eAmerican Indian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.71900826446281%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e0.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.586776859504132%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.223140495867769%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.082644628099173%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.892561983471074%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.413223140495868%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.0009\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.082644628099175%\" valign=\"bottom\"\u003e\n \u003cp\u003eAsian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.71900826446281%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e2.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.586776859504132%\" valign=\"bottom\"\u003e\n \u003cp\u003e3.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.223140495867769%\" colspan=\"2\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.082644628099173%\" valign=\"bottom\"\u003e\n \u003cp\u003e2.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.892561983471074%\" valign=\"bottom\"\u003e\n \u003cp\u003e3.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.413223140495868%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.082644628099175%\" valign=\"bottom\"\u003e\n \u003cp\u003eBlack or African American\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.71900826446281%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e14.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.586776859504132%\" valign=\"bottom\"\u003e\n \u003cp\u003e11.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.223140495867769%\" colspan=\"2\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.082644628099173%\" valign=\"bottom\"\u003e\n \u003cp\u003e12.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.892561983471074%\" valign=\"bottom\"\u003e\n \u003cp\u003e11.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.413223140495868%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.082644628099175%\" valign=\"bottom\"\u003e\n \u003cp\u003eHawaiian or Pacific Islander\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.71900826446281%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e0.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.586776859504132%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.223140495867769%\" colspan=\"2\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.082644628099173%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.892561983471074%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.413223140495868%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.082644628099175%\" valign=\"bottom\"\u003e\n \u003cp\u003eMulti-Racial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.71900826446281%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e2.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.586776859504132%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.223140495867769%\" colspan=\"2\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.082644628099173%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.892561983471074%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.413223140495868%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.082644628099175%\" valign=\"bottom\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.71900826446281%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e1.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.586776859504132%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.223140495867769%\" colspan=\"2\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.082644628099173%\" valign=\"bottom\"\u003e\n \u003cp\u003e2.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.892561983471074%\" valign=\"bottom\"\u003e\n \u003cp\u003e2.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.413223140495868%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.082644628099175%\" valign=\"bottom\"\u003e\n \u003cp\u003eUnknown/Missing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.71900826446281%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e6.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.586776859504132%\" valign=\"bottom\"\u003e\n \u003cp\u003e4.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.223140495867769%\" colspan=\"2\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.082644628099173%\" valign=\"bottom\"\u003e\n \u003cp\u003e3.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.892561983471074%\" valign=\"bottom\"\u003e\n \u003cp\u003e4.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.413223140495868%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.082644628099175%\" valign=\"bottom\"\u003e\n \u003cp\u003eWhite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.71900826446281%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e71.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.586776859504132%\" valign=\"bottom\"\u003e\n \u003cp\u003e77.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.223140495867769%\" colspan=\"2\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.082644628099173%\" valign=\"bottom\"\u003e\n \u003cp\u003e77.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.892561983471074%\" valign=\"bottom\"\u003e\n \u003cp\u003e76.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.413223140495868%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.082644628099175%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eEthnicity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.71900826446281%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.586776859504132%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.223140495867769%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.082644628099173%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.892561983471074%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.413223140495868%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.082644628099175%\" valign=\"bottom\"\u003e\n \u003cp\u003eHispanic or Latino\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.71900826446281%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e12.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.586776859504132%\" valign=\"bottom\"\u003e\n \u003cp\u003e7.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.223140495867769%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.082644628099173%\" valign=\"bottom\"\u003e\n \u003cp\u003e6.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.892561983471074%\" valign=\"bottom\"\u003e\n \u003cp\u003e7.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.413223140495868%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.0002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.082644628099175%\" valign=\"bottom\"\u003e\n \u003cp\u003eNon-Hispanic or Latino\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.71900826446281%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e84.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.586776859504132%\" valign=\"bottom\"\u003e\n \u003cp\u003e89.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.223140495867769%\" colspan=\"2\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.082644628099173%\" valign=\"bottom\"\u003e\n \u003cp\u003e91.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.892561983471074%\" valign=\"bottom\"\u003e\n \u003cp\u003e89.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.413223140495868%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.082644628099175%\" valign=\"bottom\"\u003e\n \u003cp\u003eUnknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.71900826446281%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e2.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.586776859504132%\" valign=\"bottom\"\u003e\n \u003cp\u003e2.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.223140495867769%\" colspan=\"2\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.082644628099173%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.892561983471074%\" valign=\"bottom\"\u003e\n \u003cp\u003e2.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.413223140495868%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.082644628099175%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge at Admission (Years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.71900826446281%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.586776859504132%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.223140495867769%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.082644628099173%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.892561983471074%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.413223140495868%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.082644628099175%\" valign=\"bottom\"\u003e\n \u003cp\u003e18-29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.71900826446281%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e10.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.586776859504132%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.223140495867769%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.082644628099173%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.892561983471074%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.413223140495868%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.4825\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.082644628099175%\" valign=\"bottom\"\u003e\n \u003cp\u003e30-39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.71900826446281%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e13.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.586776859504132%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.223140495867769%\" colspan=\"2\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.082644628099173%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.892561983471074%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.413223140495868%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.082644628099175%\" valign=\"bottom\"\u003e\n \u003cp\u003e40-49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.71900826446281%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e9.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.586776859504132%\" valign=\"bottom\"\u003e\n \u003cp\u003e3.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.223140495867769%\" colspan=\"2\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.082644628099173%\" valign=\"bottom\"\u003e\n \u003cp\u003e3.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.892561983471074%\" valign=\"bottom\"\u003e\n \u003cp\u003e3.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.413223140495868%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.082644628099175%\" valign=\"bottom\"\u003e\n \u003cp\u003e50-59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.71900826446281%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e13.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.586776859504132%\" valign=\"bottom\"\u003e\n \u003cp\u003e8.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.223140495867769%\" colspan=\"2\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.082644628099173%\" valign=\"bottom\"\u003e\n \u003cp\u003e8.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.892561983471074%\" valign=\"bottom\"\u003e\n \u003cp\u003e9.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.413223140495868%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.082644628099175%\" valign=\"bottom\"\u003e\n \u003cp\u003e60-69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.71900826446281%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e18.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.586776859504132%\" valign=\"bottom\"\u003e\n \u003cp\u003e19.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.223140495867769%\" colspan=\"2\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.082644628099173%\" valign=\"bottom\"\u003e\n \u003cp\u003e19.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.892561983471074%\" valign=\"bottom\"\u003e\n \u003cp\u003e19.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.413223140495868%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.082644628099175%\" valign=\"bottom\"\u003e\n \u003cp\u003e70-79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.71900826446281%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e19.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.586776859504132%\" valign=\"bottom\"\u003e\n \u003cp\u003e27.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.223140495867769%\" colspan=\"2\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.082644628099173%\" valign=\"bottom\"\u003e\n \u003cp\u003e28.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.892561983471074%\" valign=\"bottom\"\u003e\n \u003cp\u003e28.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.413223140495868%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.082644628099175%\" valign=\"bottom\"\u003e\n \u003cp\u003e80-89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.71900826446281%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e13.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.586776859504132%\" valign=\"bottom\"\u003e\n \u003cp\u003e26.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.223140495867769%\" colspan=\"2\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.082644628099173%\" valign=\"bottom\"\u003e\n \u003cp\u003e26.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.892561983471074%\" valign=\"bottom\"\u003e\n \u003cp\u003e26.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.413223140495868%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.082644628099175%\" valign=\"bottom\"\u003e\n \u003cp\u003e90+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.71900826446281%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e4.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.586776859504132%\" valign=\"bottom\"\u003e\n \u003cp\u003e12.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.223140495867769%\" colspan=\"2\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.082644628099173%\" valign=\"bottom\"\u003e\n \u003cp\u003e10.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.892561983471074%\" valign=\"bottom\"\u003e\n \u003cp\u003e10.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.413223140495868%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.082644628099175%\" valign=\"bottom\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.71900826446281%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e59.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.586776859504132%\" valign=\"bottom\"\u003e\n \u003cp\u003e74.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.223140495867769%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.082644628099173%\" valign=\"bottom\"\u003e\n \u003cp\u003e74.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.892561983471074%\" valign=\"bottom\"\u003e\n \u003cp\u003e73.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.413223140495868%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.0275\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.082644628099175%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdmit through ED\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.71900826446281%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.586776859504132%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.223140495867769%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.082644628099173%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.892561983471074%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.413223140495868%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.082644628099175%\" valign=\"bottom\"\u003e\n \u003cp\u003eNo\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.71900826446281%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e53.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.586776859504132%\" valign=\"bottom\"\u003e\n \u003cp\u003e50.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.223140495867769%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.082644628099173%\" valign=\"bottom\"\u003e\n \u003cp\u003e46.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.892561983471074%\" valign=\"bottom\"\u003e\n \u003cp\u003e51.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.413223140495868%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.082644628099175%\" valign=\"bottom\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.71900826446281%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e46.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.586776859504132%\" valign=\"bottom\"\u003e\n \u003cp\u003e49.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.223140495867769%\" colspan=\"2\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.082644628099173%\" valign=\"bottom\"\u003e\n \u003cp\u003e53.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.892561983471074%\" valign=\"bottom\"\u003e\n \u003cp\u003e48.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.413223140495868%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.082644628099175%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdmit Type\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.71900826446281%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.586776859504132%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.223140495867769%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.082644628099173%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.892561983471074%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.413223140495868%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.082644628099175%\" valign=\"bottom\"\u003e\n \u003cp\u003eElective\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.71900826446281%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e22.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.586776859504132%\" valign=\"bottom\"\u003e\n \u003cp\u003e9.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.223140495867769%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.082644628099173%\" valign=\"bottom\"\u003e\n \u003cp\u003e9.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.892561983471074%\" valign=\"bottom\"\u003e\n \u003cp\u003e9.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.413223140495868%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.082644628099175%\" valign=\"bottom\"\u003e\n \u003cp\u003eEmergency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.71900826446281%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e70.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.586776859504132%\" valign=\"bottom\"\u003e\n \u003cp\u003e85.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.223140495867769%\" colspan=\"2\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.082644628099173%\" valign=\"bottom\"\u003e\n \u003cp\u003e86.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.892561983471074%\" valign=\"bottom\"\u003e\n \u003cp\u003e86.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.413223140495868%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.082644628099175%\" valign=\"bottom\"\u003e\n \u003cp\u003eTrauma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.71900826446281%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e0.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.586776859504132%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.223140495867769%\" colspan=\"2\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.082644628099173%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.892561983471074%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.413223140495868%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.082644628099175%\" valign=\"bottom\"\u003e\n \u003cp\u003eUrgent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.71900826446281%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e6.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.586776859504132%\" valign=\"bottom\"\u003e\n \u003cp\u003e3.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.223140495867769%\" colspan=\"2\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.082644628099173%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.892561983471074%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.413223140495868%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.082644628099175%\" valign=\"bottom\"\u003e\n \u003cp\u003eUnknown/Missing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.71900826446281%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e0.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.586776859504132%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.223140495867769%\" colspan=\"2\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.082644628099173%\" valign=\"bottom\"\u003e\n \u003cp\u003e2.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.892561983471074%\" valign=\"bottom\"\u003e\n \u003cp\u003e3.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.413223140495868%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.082644628099175%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eDischarge Status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.71900826446281%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.586776859504132%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.223140495867769%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.082644628099173%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.892561983471074%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.413223140495868%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.082644628099175%\" valign=\"bottom\"\u003e\n \u003cp\u003eExpired\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.71900826446281%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e0.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.586776859504132%\" valign=\"bottom\"\u003e\n \u003cp\u003e100.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.223140495867769%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.082644628099173%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.892561983471074%\" valign=\"bottom\"\u003e\n \u003cp\u003e100.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.413223140495868%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.082644628099175%\" valign=\"bottom\"\u003e\n \u003cp\u003eHome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.71900826446281%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e66.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.586776859504132%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.223140495867769%\" colspan=\"2\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.082644628099173%\" valign=\"bottom\"\u003e\n \u003cp\u003e35.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.892561983471074%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.413223140495868%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.082644628099175%\" valign=\"bottom\"\u003e\n \u003cp\u003eHome Health\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.71900826446281%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e14.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.586776859504132%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.223140495867769%\" colspan=\"2\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.082644628099173%\" valign=\"bottom\"\u003e\n \u003cp\u003e24.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.892561983471074%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.413223140495868%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.082644628099175%\" valign=\"bottom\"\u003e\n \u003cp\u003eIntermediate Care\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.71900826446281%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e0.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.586776859504132%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.223140495867769%\" colspan=\"2\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.082644628099173%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.892561983471074%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.413223140495868%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.082644628099175%\" valign=\"bottom\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.71900826446281%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e2.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.586776859504132%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.223140495867769%\" colspan=\"2\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.082644628099173%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.892561983471074%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.413223140495868%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.082644628099175%\" valign=\"bottom\"\u003e\n \u003cp\u003eRehab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.71900826446281%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e2.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.586776859504132%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.223140495867769%\" colspan=\"2\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.082644628099173%\" valign=\"bottom\"\u003e\n \u003cp\u003e3.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.892561983471074%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.413223140495868%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.082644628099175%\" valign=\"bottom\"\u003e\n \u003cp\u003eSkilled Nursing Facility\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.71900826446281%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e12.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.586776859504132%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.223140495867769%\" colspan=\"2\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.082644628099173%\" valign=\"bottom\"\u003e\n \u003cp\u003e32.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.892561983471074%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.413223140495868%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.082644628099175%\" valign=\"bottom\"\u003e\n \u003cp\u003eTransfer to Another Facility\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.71900826446281%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e1.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.586776859504132%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.223140495867769%\" colspan=\"2\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.082644628099173%\" valign=\"bottom\"\u003e\n \u003cp\u003e2.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.892561983471074%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.413223140495868%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.082644628099175%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdmit Rothman Index\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.71900826446281%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.586776859504132%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.223140495867769%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.082644628099173%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.892561983471074%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.413223140495868%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.082644628099175%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.71900826446281%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e0.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.586776859504132%\" valign=\"bottom\"\u003e\n \u003cp\u003e12.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.223140495867769%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.082644628099173%\" valign=\"bottom\"\u003e\n \u003cp\u003e3.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.892561983471074%\" valign=\"bottom\"\u003e\n \u003cp\u003e3.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.413223140495868%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.9774\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.082644628099175%\" valign=\"bottom\"\u003e\n \u003cp\u003e20-29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.71900826446281%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e1.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.586776859504132%\" valign=\"bottom\"\u003e\n \u003cp\u003e10.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.223140495867769%\" colspan=\"2\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.082644628099173%\" valign=\"bottom\"\u003e\n \u003cp\u003e8.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.892561983471074%\" valign=\"bottom\"\u003e\n \u003cp\u003e8.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.413223140495868%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.082644628099175%\" valign=\"bottom\"\u003e\n \u003cp\u003e30-39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.71900826446281%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e2.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.586776859504132%\" valign=\"bottom\"\u003e\n \u003cp\u003e13.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.223140495867769%\" colspan=\"2\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.082644628099173%\" valign=\"bottom\"\u003e\n \u003cp\u003e14.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.892561983471074%\" valign=\"bottom\"\u003e\n \u003cp\u003e14.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.413223140495868%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.082644628099175%\" valign=\"bottom\"\u003e\n \u003cp\u003e40-49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.71900826446281%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e4.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.586776859504132%\" valign=\"bottom\"\u003e\n \u003cp\u003e16.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.223140495867769%\" colspan=\"2\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.082644628099173%\" valign=\"bottom\"\u003e\n \u003cp\u003e17.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.892561983471074%\" valign=\"bottom\"\u003e\n \u003cp\u003e18.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.413223140495868%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.082644628099175%\" valign=\"bottom\"\u003e\n \u003cp\u003e50-59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.71900826446281%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e8.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.586776859504132%\" valign=\"bottom\"\u003e\n \u003cp\u003e15.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.223140495867769%\" colspan=\"2\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.082644628099173%\" valign=\"bottom\"\u003e\n \u003cp\u003e17.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.892561983471074%\" valign=\"bottom\"\u003e\n \u003cp\u003e17.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.413223140495868%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.082644628099175%\" valign=\"bottom\"\u003e\n \u003cp\u003e60-69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.71900826446281%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e14.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.586776859504132%\" valign=\"bottom\"\u003e\n \u003cp\u003e14.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.223140495867769%\" colspan=\"2\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.082644628099173%\" valign=\"bottom\"\u003e\n \u003cp\u003e17.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.892561983471074%\" valign=\"bottom\"\u003e\n \u003cp\u003e17.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.413223140495868%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.082644628099175%\" valign=\"bottom\"\u003e\n \u003cp\u003e70-79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.71900826446281%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e21.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.586776859504132%\" valign=\"bottom\"\u003e\n \u003cp\u003e10.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.223140495867769%\" colspan=\"2\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.082644628099173%\" valign=\"bottom\"\u003e\n \u003cp\u003e12.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.892561983471074%\" valign=\"bottom\"\u003e\n \u003cp\u003e12.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.413223140495868%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.082644628099175%\" valign=\"bottom\"\u003e\n \u003cp\u003e80+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.71900826446281%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e47.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.586776859504132%\" valign=\"bottom\"\u003e\n \u003cp\u003e6.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.223140495867769%\" colspan=\"2\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.082644628099173%\" valign=\"bottom\"\u003e\n \u003cp\u003e7.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.892561983471074%\" valign=\"bottom\"\u003e\n \u003cp\u003e7.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.413223140495868%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.082644628099175%\" valign=\"bottom\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.71900826446281%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e75.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.586776859504132%\" valign=\"bottom\"\u003e\n \u003cp\u003e46.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.223140495867769%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.082644628099173%\" valign=\"bottom\"\u003e\n \u003cp\u003e52.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.892561983471074%\" valign=\"bottom\"\u003e\n \u003cp\u003e52.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.413223140495868%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.4509\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.082644628099175%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eSurgery During Stay\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.71900826446281%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.586776859504132%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.223140495867769%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.082644628099173%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.892561983471074%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.413223140495868%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.082644628099175%\" valign=\"bottom\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.71900826446281%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e86.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.586776859504132%\" valign=\"bottom\"\u003e\n \u003cp\u003e91.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.223140495867769%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.082644628099173%\" valign=\"bottom\"\u003e\n \u003cp\u003e89.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.892561983471074%\" valign=\"bottom\"\u003e\n \u003cp\u003e90.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.413223140495868%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.0863\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.082644628099175%\" valign=\"bottom\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.71900826446281%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e13.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.586776859504132%\" valign=\"bottom\"\u003e\n \u003cp\u003e8.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.223140495867769%\" colspan=\"2\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.082644628099173%\" valign=\"bottom\"\u003e\n \u003cp\u003e10.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.892561983471074%\" valign=\"bottom\"\u003e\n \u003cp\u003e9.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.413223140495868%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.082644628099175%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdmit Unit Type\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.71900826446281%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.586776859504132%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.223140495867769%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.082644628099173%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.892561983471074%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.413223140495868%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.082644628099175%\" valign=\"bottom\"\u003e\n \u003cp\u003eIntermediate Care\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.71900826446281%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e10.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.586776859504132%\" valign=\"bottom\"\u003e\n \u003cp\u003e24.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.223140495867769%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.082644628099173%\" valign=\"bottom\"\u003e\n \u003cp\u003e17.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.892561983471074%\" valign=\"bottom\"\u003e\n \u003cp\u003e23.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.413223140495868%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.082644628099175%\" valign=\"bottom\"\u003e\n \u003cp\u003eRoutine Care\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.71900826446281%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e82.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.586776859504132%\" valign=\"bottom\"\u003e\n \u003cp\u003e71.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.223140495867769%\" colspan=\"2\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.082644628099173%\" valign=\"bottom\"\u003e\n \u003cp\u003e76.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.892561983471074%\" valign=\"bottom\"\u003e\n \u003cp\u003e72.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.413223140495868%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.082644628099175%\" valign=\"bottom\"\u003e\n \u003cp\u003eTelemetry\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.71900826446281%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e1.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.586776859504132%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.223140495867769%\" colspan=\"2\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.082644628099173%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.892561983471074%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.413223140495868%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.082644628099175%\" valign=\"bottom\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.71900826446281%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e5.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.586776859504132%\" valign=\"bottom\"\u003e\n \u003cp\u003e3.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.223140495867769%\" colspan=\"2\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.082644628099173%\" valign=\"bottom\"\u003e\n \u003cp\u003e5.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.892561983471074%\" valign=\"bottom\"\u003e\n \u003cp\u003e3.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.413223140495868%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 2\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003ePost-Match Univariate Analysis.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNot Expired\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eExpired\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep Value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eOverall Length of Stay (Days)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStd Dev\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMedian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eICU Length of Stay (Days)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0265\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStd Dev\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMedian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.6416\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eIn multivariable analysis, after controlling for patient and visit characteristics, patients who expired were predicted to have a 51% longer overall hospital LOS (Incident Rate Ratio (IRR)\u0026thinsp;=\u0026thinsp;1.505, 95% CI\u0026thinsp;=\u0026thinsp;1.458\u0026ndash;1.555, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Other factors associated with longer total LOS included urgent (IRR\u0026thinsp;=\u0026thinsp;1.380, 95% CI\u0026thinsp;=\u0026thinsp;1.246\u0026ndash;1.530, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) and emergency admissions (IRR\u0026thinsp;=\u0026thinsp;1.152, 95% CI\u0026thinsp;=\u0026thinsp;1.091\u0026ndash;1.216, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) as well as surgery occurring during the hospital stay (IRR\u0026thinsp;=\u0026thinsp;1.953, 95% CI\u0026thinsp;=\u0026thinsp;1.852\u0026ndash;2.059, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Factors associated with shorter hospital LOS were female sex (IRR\u0026thinsp;=\u0026thinsp;0.922 (0.893\u0026ndash;0.951, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), higher RI scores which represent lower acuity (IRR\u0026thinsp;=\u0026thinsp;0.997, 95% CI\u0026thinsp;=\u0026thinsp;0.996\u0026ndash;0.998, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) and higher ages (IRR\u0026thinsp;=\u0026thinsp;0.989, 95% CI\u0026thinsp;=\u0026thinsp;0.987\u0026ndash;0.990, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). IRRs for all variables are shown in Table\u0026nbsp;\u003cspan\u003e3\u003c/span\u003e.\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 3\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eNegative binomial regression model for hospital length of stay.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eParameter\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eValue\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eReference Value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEstimate\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep Value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eIRR (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIntercept\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.995\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMortality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.409\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.505 (1.458\u0026ndash;1.555)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.081\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.922 (0.893\u0026ndash;0.951)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAdmit Rothman Index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.997 (0.996\u0026ndash;0.998)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePatient Age (Years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.989 (0.987\u0026ndash;0.990)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAdmission type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEmergency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eElective\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.141\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.152 (1.091\u0026ndash;1.216)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAdmission type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTrauma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eElective\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.060\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.533\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.942 (0.780\u0026ndash;1.137)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAdmission type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eElective\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.469\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.599 (1.082\u0026ndash;2.362)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAdmission type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrgent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eElective\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.322\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.380 (1.246\u0026ndash;1.530)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAdmit Unit Type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIntermediate Care\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRoutine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.851\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.004 (0.965\u0026ndash;1.044)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAdmit Unit Type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTelemetry\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRoutine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.255\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.775 (0.658\u0026ndash;0.911)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSurgery During Stay\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.669\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.953 (1.852\u0026ndash;2.059)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eIn-hospital mortality was associated with a 33% increase in ICU LOS (IRR\u0026thinsp;=\u0026thinsp;1.334, 95% CI\u0026thinsp;=\u0026thinsp;1.180\u0026ndash;1.508, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Surgery during stay (IRR\u0026thinsp;=\u0026thinsp;1.508, 95% CI\u0026thinsp;=\u0026thinsp;1.345\u0026ndash;1.690, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) was also associated with a predicted increase in LOS. Emergency admissions (IRR\u0026thinsp;=\u0026thinsp;0.801, 95% CI\u0026thinsp;=\u0026thinsp;0.689\u0026ndash;0.932, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004) were associated with shorter ICU LOS along with higher patient age (IRR\u0026thinsp;=\u0026thinsp;0.987, 95% CI\u0026thinsp;=\u0026thinsp;0.984\u0026ndash;0.991, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). IRRs for all variables are shown in Table\u0026nbsp;\u003cspan\u003e4\u003c/span\u003e.\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 4\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eNegative binomial regression model for ICU length of stay.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eParameter\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eValue\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eReference Value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEstimate\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep Value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eIRR (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIntercept\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.736\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMortality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.288\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.334 (1.180\u0026ndash;1.508)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.075\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.069\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.928 (0.855\u0026ndash;1.006)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAdmit Rothman Index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.595\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.001 (0.998\u0026ndash;1.003)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePatient Age (Years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.987 (0.984\u0026ndash;0.991)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAdmission type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEmergency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eElective\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.222\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.801 (0.689\u0026ndash;0.932)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAdmission type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTrauma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eElective\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.249\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.310\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.779 (0.482\u0026ndash;1.261)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAdmission type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eElective\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.395\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.476\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.484 (0.501\u0026ndash;4.396)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAdmission type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrgent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eElective\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.616\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.942 (0.748\u0026ndash;1.188)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAdmit Unit Type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIntermediate Care\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eRoutine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.000 (0.908\u0026ndash;1.101)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAdmit Unit Type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTelemetry\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eRoutine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.092\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.677\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.912 (0.592\u0026ndash;1.406)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSurgery During Stay\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.411\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.508 (1.345\u0026ndash;1.690)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eRecognizing a growing need to justify expenditures on quality improvement programs that have a focus on avoidable mortality, our study sought to understand the association of mortality with overall LOS and ICU LOS. Specifically, we evaluated patients admitted to lower level of care settings, a population targeted by many mortality reduction programs including those using EWS\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWe found that patients who expired had significantly longer LOS, both overall and in the ICU. We also found that patients with urgent and emergency admissions, as well as surgical procedures, had longer LOS, and the latter two factors were also associated with longer ICU LOS. Complexity of correlations between mortality and LOS have been reported elsewhere, and causal relationships and interacting clinical considerations are not simple to delineate.\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e In light of the range of factors associated with LOS measures, a strength of our analysis is its patient-level nature\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e and use of the RI to ensure cohorts were of comparable acuity at the time of admission to account for the relation between baseline physiological acuity and mortality risk.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eIndividual hospitals may find that the magnitude of the LOS difference between expired and non-expired patients in their populations is different from what we report here, and indeed additional matching criteria may be considered for deriving comparison cohorts. However, the magnitude of the difference we have found in both overall and ICU LOS for patients with comparable physiologic acuity on admission as measured by the RI patient condition score strongly suggests that a non-negligible LOS difference exists.\u003c/p\u003e \u003cp\u003eHospital-based quality improvement initiatives face a growing need to demonstrate a robust business case justification and an argument for cost effectiveness. In particular, quality initiatives that require significant investment, such as operationalizing EWS for more timely detection and intervention on deteriorating patients, can have laudable quality goals yet struggle to translate even unambiguous success into a clear financial benefit. Hence, we posit that a rigorously established association between mortality and LOS may provide a means for organizations to extrapolate a reduction in both overall and ICU LOS from reductions in inpatient mortality. This would then allow readily available cost-per-day models to be brought to bear for the purpose of computing a financial ROI attributable to a reduction in avoidable mortality.\u003c/p\u003e \u003cp\u003eFurthermore, we note that the ability to infer a reduction in LOS from a reduction in avoidable mortality circumvents two associated LOS measurement challenges. First, while the LOS savings from a program focused on reducing avoidable mortality may be substantial within the specific target population, the volume of this target population (deaths and avoided deaths) is small (typically a few percent) relative to the total acute-care population, making it quantitatively challenging to discern an LOS change due to mortality reduction initiatives within total population-level LOS statistics. Second, variations in population acuity over time (which may be as simple as a more severe flu season from one year to another), variations in populations served (e.g. shifts in regional acute-care capacity, staffing and specialty service capacity, inter-hospital transfer patterns, ambulance diversion rates, etc.) and the fact that multiple contemporaneous operational changes are frequently made which have the potential, if not explicit aim, of influencing LOS, make causal attribution of changes to population-level LOS an uncertain proposition. Identifying and isolating initiatives likely to bear on mortality rates is a more manageable task, while possible acuity changes in a focused population can be accounted for using risk adjustment or acuity matching methodologies.\u003c/p\u003e \u003cp\u003eOur study has limitations. We lacked data on patient goals of care, comfort measure status, and advanced directives which, if included as covariates, could nuance the populations used in the matched cohorts. We did not include diagnosis in the analysis, and while the Rothman Index was built and validated as a diagnosis agnostic patient condition acuity score, we recognize that at a granular patient level the course of care, decisions related to treatment in the ICU, and overall LOS may have a dependence on diagnosis and comorbidities. Additionally, while we conducted the matching process separately for each health system, combining data in the final analysis may have obscured facility-level differences in LOS measures between cohorts.\u003c/p\u003e \u003cp\u003eWe anticipate future work will entail expanding the methodological approach presented in this work to include segmenting populations by clinical diagnosis and/or comorbidities. Extending our analysis to evaluate the costs of care (or cost-per-day) specific to the patient sub-populations in our matched cohort groups may also elucidate important differences between generalized cost-per-day figures and the costs pertaining to our analysis populations (i.e. average costs are likely to be different in selected sub-population as a function of variations in acuity, treatment plans, goals of care etc.).\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eFinancial pressures increasingly require that quality initiatives demonstrate a positive ROI. Reducing avoidable mortality is a well-established quality of care goal, but one which requires an investment of resources that is not straightforward to justify on a purely financial basis. This work demonstrates a statistically rigorous approach to inferring LOS reductions from mortality reductions as a possible path to calculating a financial ROI for programs that are successful in their quest to improve avoidable mortality rates.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eEWS\u0026nbsp; \u0026nbsp;\u0026nbsp;Early Warning Score\u003c/p\u003e\n\u003cp\u003eLOS\u0026nbsp; \u0026nbsp;\u0026nbsp;Length of Stay\u003cbr\u003eICU\u0026nbsp; \u0026nbsp; \u0026nbsp;Intensive Care Unit\u003cbr\u003eRI\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Rothman Index\u003c/p\u003e\n\u003cp\u003eROI \u0026nbsp; \u0026nbsp; Return on Investment\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJ.B. and K.B. conceived and designed the study; K.B. performed data extraction and preparation and K.B. and S.M. conducted statistical analyses; J.B., K.B. and S.M. interpreted the results and J.B. and K.B. drafted the manuscript. All authors approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthors are employees of Spacelabs Healthcare. Neither Spacelabs nor Spacelabs affiliated authors received additional funding or compensation for this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDue to confidentiality and data use agreements, the datasets analyzed in the current study are not publicly available. Requests to access the datasets for bona fide research purposes should be directed to the corresponding author.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable as this work entailed retrospective research using health information which was de-identified in accordance with 45 CFR 164.502(d) and 45 CFR 164.514(a)-(b).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors are employed by Spacelabs Healthcare, the commercial entity behind the Rothman Index patient condition score which was used in this analysis.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eTruxillo TM, Schubert A, Guthrie R. 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Case-mix adjusted hospital mortality is a poor proxy for preventable mortality: a modelling study. \u003cem\u003eBMJ Qual Saf\u003c/em\u003e. 2012;21(12):1052-1056. doi:10.1136/bmjqs-2012-001202\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-health-services-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bhsr","sideBox":"Learn more about [BMC Health Services Research](http://bmchealthservres.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/BHSR/default.aspx","title":"BMC Health Services Research","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Mortality, Length of Stay, Return on Investment, Quality Improvement, Early Warning Score System","lastPublishedDoi":"10.21203/rs.3.rs-3934554/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3934554/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe economics of healthcare increasingly compels hospitals to accompany quality initiatives with a financial business case. Quality programs are frequently implemented with the aim of identifying clinical deterioration and reducing avoidable mortality. However, low rates of inpatient mortality with a diverse etiology make quantifying cost savings from avoidable mortality reduction challenging. To establish a relationship between mortality and length of stay, a commonly accepted indicator of hospital cost, we evaluated total hospital and intensive care unit length of stay for inpatients who expired compared to similar patients who did not expire during their hospital stay.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe conducted a retrospective propensity-matched cohort analysis of hospital inpatients who did and did not expire using de-identified data from 24 hospitals representing six health systems across the United States. In addition to demographic measures, the Rothman Index patient condition score was used to ensure cohorts were matched in physiologic acuity. Multivariable regression models were used to estimate the impact of mortality on the primary outcomes of total and ICU length of stay.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eMatched cohorts of patients who did and did not expire each comprised 6,129 patients. Patients who expired had longer mean overall hospital length of stay (LOS) compared to those who did not (13.3 vs 8.3 days, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) as well as longer mean ICU LOS (7.2 vs 5.7 days, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0265). Multivariable general linear models identified a 51% increase in predicted LOS and 33% increase in ICU LOS for patients who expired in the hospital.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eAs length of stay measures are routinely quantified in financial terms by hospitals, this work suggests it may be possible to translate mortality reductions into length of stay reductions as an inferential step in deriving a financial return on investment for mortality-focused quality of care initiatives.\u003c/p\u003e","manuscriptTitle":"The potential for in-patient mortality reductions to drive cost savings through decreases in hospital length of stay and intensive care unit utilization: a propensity matched cohort analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-16 16:48:14","doi":"10.21203/rs.3.rs-3934554/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2024-02-26T19:47:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"1d89513d-5d0d-4495-ab36-79ee1552fd3b","date":"2024-02-19T15:00:24+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-02-18T12:01:22+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-02-15T17:05:45+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-02-15T07:55:57+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-02-15T07:55:57+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Health Services Research","date":"2024-02-06T17:01:20+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-health-services-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bhsr","sideBox":"Learn more about [BMC Health Services Research](http://bmchealthservres.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/BHSR/default.aspx","title":"BMC Health Services Research","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"718aa118-8f0c-451e-85ad-86dc57c71825","owner":[],"postedDate":"February 16th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-02-16T16:48:14+00:00","versionOfRecord":[],"versionCreatedAt":"2024-02-16 16:48:14","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3934554","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3934554","identity":"rs-3934554","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}